In studying college effects, an input-output model is commonly used in which student input is controlled by using regression analysis to compute an "expected" output. The part correlation of the college environment variable and the output with input variance removed only from the output is interpreted as a measure of the college effect. However, this is not the most useful procedure that may b e used since part (or partial) correlation may severely underestimate the magnitude of the true college effect. Interpreted within a causal model, partial regression coefficients appear to be a generally more satisfactory measure of college effects. Four models are used to illustrate the advantages of using partial regression coefficients in a causal framework. Another advantage in using these coefficients is that they have greater stability across different units of measurement. (Author)